CHARTS

Which countries dominate the world’s dinner tables?

Bar divergent

Photo by Jorge Zapata on Unsplash

Photo by Jorge Zapata on Unsplash

If your mother cooks Italian food, why should you go to a restaurant?
— Martin Scorsese, Director

A study published in the the Journal of Cultural Economics, Dining out as cultural trade, used data from TripAdvisor to examine restaurant cuisines, combined with Euromonitor data on overall and fast-food expenditure. The paper calculated the implicit trade patterns in global cuisines for 52 destination countries. The research was presented in a Daily Chart segment of the Economist using a bar chart with divergent values. The chart is designed to reveal how domestic and foreign food consumption profiles can identify which countries have the greatest influence on the world’s cuisine preferences.

  • Domestic consumption of foreign cuisine is treated as an import
  • Foreign consumption of domestic cuisine is treated as an export

Ingest the data

Cuisine net exports excluding and including fast food

# Load data
df_exports <- read.csv("archetypes/world-cuisine-preferences/table-6-cuisine-net-exports-excluding-fast-food.csv", header = TRUE, stringsAsFactors = FALSE)
df_exports
df_fast_food <- read.csv("archetypes/world-cuisine-preferences/table-6-cuisine-net-exports-including-fast-food.csv", header = TRUE, stringsAsFactors = FALSE)
df_fast_food

Wrangle and analyze

Refactor to set stack sort order

  • Cast numeric types
  • Calculate Net.Exports
  • Ordering Country in descending order by their Net.Exports
  • Create a column “Color” to color code the Importers and Exporters
  • Separate the Importers and Exporters to support labeling
# Cast column type
df_exports$Exports <- as.numeric(gsub(",", "", df_exports$Exports))
df_exports$Imports <- as.numeric(gsub(",", "", df_exports$Imports))

# Cast column type
df_fast_food$Exports <- as.numeric(gsub(",", "", df_fast_food$Exports))
df_fast_food$Imports <- as.numeric(gsub(",", "", df_fast_food$Imports))

# Calculate the net exports and fill in the fourth column
df_exports$Net.exports <- df_exports$Exports-df_exports$Imports

df_fast_food$Net.exports <- df_fast_food$Exports-df_fast_food$Imports

# Remove all missing data
df_exports <- df_exports %>% filter(Net.exports!='NA')

df_fast_food <-df_fast_food %>% filter(Net.exports!='NA')

# Change order of factors to sort from highest to lowest
df_exports <- df_exports %>% mutate(Country = factor(Country))
df_exports$Country <- fct_reorder(df_exports$Country, df_exports$Net.exports, max)

df_fast_food <- df_fast_food %>% mutate(Country = factor(Country))
df_fast_food$Country <- fct_reorder(df_fast_food$Country, df_fast_food$Net.exports, max)

# Make colors based on value threshold
df_exports$Color <- ifelse(df_exports$Net.exports > 0, "Exporter", "Importer")
df_exports <- df_exports %>% mutate(Color = factor(Color))

df_fast_food$Color <- ifelse(df_fast_food$Net.exports > 0, "Exporter", "Importer")
df_fast_food <- df_fast_food %>% mutate(Color = factor(Color))

# Filter df into two parts for labels later
df_exports_l <- df_exports %>% filter( df_exports$Net.exports > 0 )
df_exports_r <- df_exports %>% filter( df_exports$Net.exports < 0 )

df_fast_food_l <- df_fast_food %>% filter( df_fast_food$Net.exports > 0 )
df_fast_food_r <- df_fast_food %>% filter( df_fast_food$Net.exports < 0 )

df_exports
df_fast_food

Plot

shared theme and palette

# Common theme for both charts
theme_opts <- theme(
    legend.position='none', 
    panel.background = element_rect(fill = "white", colour = "white"), 
    #axis.text.x=element_blank(), 
    #axis.ticks.x=element_blank(), 
    axis.title.x = element_blank(),
    axis.text.y=element_blank(), 
    axis.ticks.y=element_blank(),   
    axis.title.y = element_blank(),
    # panel.grid.major.x = element_blank(),
    panel.grid.major.x = element_line(size = 0.5, linetype = 'solid', colour = "#b0bfc8"),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank()
    )

color_palette <- c("Exporter" = "#076fa1", "Importer" = "#2fc1d3")

excluding fast food

# Make the plot
v1 <- ggplot( df_exports, aes( x = Net.exports, y = Country, fill = Color ) ) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = color_palette) +
  # Country Label
  geom_text( data = df_exports_l, size = 4, label = df_exports_l$Country, x = -110, y = df_exports_l$Country, hjust = 1, color = 'gray30' ) +
  geom_text( data = df_exports_r, size = 4, label = df_exports_r$Country, x = 200, y = df_exports_r$Country, hjust = 0, color= 'gray30' ) +
  # Value Label
  geom_text( data = df_exports_l, size = 4, label = df_exports_l$Net.exports, hjust = 0, position = position_nudge(x = 125), color = 'gray30') +
  geom_text( data = df_exports_r, size = 4, label = df_exports_r$Net.exports, hjust = 1, position = position_nudge(x = -125), color = 'gray30') +
  # Manual Annotations
  annotate( geom = "text", x = 51000, y = 'Turkey', label = 'Net\nExporters', color = "#076fa1", size = 6, hjust = 0) +
  annotate( geom = "text", x = -51000, y = 'Australia' , label = 'Net\nImporters', color = "#2fc1d3", size = 6, hjust = 1) +
  # Red Line
  geom_vline( xintercept = 0, colour = "red", linetype = "solid", size = 1.1 ) +
  # Top Axis
  scale_x_continuous(limits = c(-150000, 175000), breaks=seq(-150000,175000, 25000), sec.axis = dup_axis()) +
  # Title and Caption
  labs(title = "Unbalanced diets", subtitle = "Cuisine Net Exports Excluding Fast Food") +
  #labs()+
  theme_minimal() +
  theme_opts

girafe(ggobj = v1, width_svg = 1280/72, height_svg = 720/72,
       options = list(opts_sizing(rescale = TRUE, width = 1.0))
)

including fast food

# Make the plot
v2 <- ggplot( df_fast_food, aes( x = Net.exports, y = Country, fill = Color ) ) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = color_palette) +
  # Country Label
  geom_text( data = df_fast_food_l, size = 4, label = df_fast_food_l$Country, x = -110, y = df_fast_food_l$Country, hjust = 1, color = 'gray30' ) +
  geom_text( data = df_fast_food_r, size = 4, label = df_fast_food_r$Country, x = 200, y = df_fast_food_r$Country, hjust = 0, color= 'gray30' ) +
  # Value Label
  geom_text( data = df_fast_food_l, size = 4, label = df_fast_food_l$Net.exports, x = df_fast_food_l$Net.exports,  y = df_fast_food_l$Country, hjust = 0, position = position_nudge(x = 125), color = 'gray30') +
  geom_text( data = df_fast_food_r, size = 4, label = df_fast_food_r$Net.exports, x = df_fast_food_r$Net.exports,  y = df_fast_food_r$Country, hjust = 1, position = position_nudge(x = -125), color = 'gray30') +
  # Manual Annotations
  annotate( geom = "text", x = 51000, y = 'France', label = 'Net\nExporters', color = "#076fa1", size = 6, hjust = 0) +
  annotate( geom = "text", x = -51000, y = 'Australia' , label = 'Net\nImporters', color = "#2fc1d3", size = 6, hjust = 1) +
  # Red Line
  geom_vline( xintercept = 0, colour = "red", linetype = "solid", size = 1.1 ) +
  # Top Axis
  scale_x_continuous(limits = c(-75000, 175000), breaks=seq(-75000,175000, 25000), sec.axis = dup_axis()) +
  # Title and Caption
  labs(title = "Unbalanced diets", subtitle = "Cuisine Net Exports Including Fast Food ($bn)") +
  theme_minimal() +
  theme_opts

girafe(ggobj = v2, width_svg = 1280/72, height_svg = 720/72,
       options = list(opts_sizing(rescale = TRUE, width = 1.0))
)

Save plot

as vector or raster image

References

citations for narrative and data sources

## [1] T. Economist. _Which countries dominate the world’s dinner
## tables?_ The Economist, Aug. 2019. <URL:
## https://www.economist.com/graphic-detail/2019/08/23/which-countries-dominate-the-worlds-dinner-tables>
## (visited on 02/15/2021).
## 
## [2] J. Waldfogel and N. B. O. E. Research. _Dining out as cultural
## trade_. National Bureau Of Economic Research, 2019.
Use

This work is licensed under
Attribution 4.0 International
CC BY 4.0

Source

Creation

Hand crafted with ♥ by
the Kamino Crew